CN113012105A - Yarn hairiness detection and rating method based on image processing - Google Patents
Yarn hairiness detection and rating method based on image processing Download PDFInfo
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- G06T2207/30242—Counting objects in image
Abstract
The invention relates to a yarn hairiness detection and rating method based on image processing, which is used for accurately detecting characteristic parameters of yarn hairiness and comprises the steps of utilizing a digital video microscope RH2000 to collect images, carrying out wavelet illumination removal, binarization, noise removal and refinement connection processing on the collected images to obtain hairiness images, dividing each hairiness in the images into different areas, marking label values, respectively counting the number of pixels and the number of hairiness of each hairiness, finding out the corresponding relation between the number of pixels and the length of the hairiness, converting the pixels of the hairiness into the length of the hairiness, and further utilizing the length of the hairiness and the number of the hairiness to realize the hairiness rating. The image processing result is consistent with the visual hairiness result, and the accuracy of hairiness detection can be improved by the method.
Description
Technical Field
The invention relates to the field of yarn evenness detection methods, in particular to a yarn hairiness detection and rating method based on image processing.
Background
The method for detecting the hair feather comprises a photoelectric method and an artificial visual counting method, the photoelectric type hair feather method is high in detection efficiency but affected by the shape of the hair feather, the projection length of the hair feather in the vertical direction in a two-dimensional plane is detected instead of the absolute length of the hair feather, the bent hair feather cannot be effectively counted, and the result is not accurate enough. The manual visual counting method has the defects of less sampling and low efficiency,
disclosure of Invention
The invention aims to provide a yarn hairiness detection and rating method based on image processing, which has accurate detection result and high efficiency.
The yarn hairiness detection and rating method based on image processing comprises the following steps:
step 1, collecting an original yarn image;
step 3, performing multilayer wavelet decomposition reconstruction on the filtered yarn evenness image to approximately reconstruct an illumination information layer, and eliminating the influence of uneven illumination through arithmetic operation to obtain a yarn image;
step 4, performing opening operation on the yarn image without the illumination influence in the step 3 to obtain a yarn main body, and extracting a hairiness image without the yarn main body by using mathematical operation; filtering and denoising the signal;
and 5, carrying out binarization on the yarn trunk image obtained in the step 4 by using an improved Dajin algorithm to obtain a clear hairiness image.
and 7, dividing each hairiness in the binary hairiness image matrix into different areas for statistics, and setting the connected area to be four, so that the phenomenon that the crossed hairiness is divided into one connected area can be avoided. All the hair feather pixels in the pixel matrix meeting the requirement of the preset connected area are marked as the same label value, the hair feather pixels in different connected areas are marked as different label values, the total label number num is returned, and the labeled hair feather image matrix L is obtained. And circularly traversing the number of the hairiness pixels in different areas in the pixel matrix. Calculating and storing the number of the hairiness pixels of each connected region and the number of the connected regions corresponding to each pixel number to obtain a matrix S;
step 8, measuring the length Ls of the yarn in the final hair feather image, wherein the diameter Lv and V of the hair feather represent the horizontal resolution of the final hair feather image, converting the length of the hair feather into a pixel number interval by finding the corresponding relation between the pixel number and the length, utilizing a formula sum to be L + Lv + V ^2/Ls ^2, counting the matrix value of the pixel number in the corresponding interval of the matrix S by utilizing a circulation algorithm to obtain the hair feather quantity corresponding to different lengths, for example, when the number of the hair feather length in the interval of [ e mm, f mm ] is calculated, converting e mm into a pixel by the formula, converting f mm into b pixel, the value range of the sum of the hair feather length in the interval of [ e mm, f mm ] is [ a, b ], counting the number m1 of the hair number in the interval of the hair feather pixel number in the interval of [ a, b ] in the matrix S, and then m1 represents the length of the hair feather in the interval of [ e mm, f mm ] total number within the interval;
and 9, calculating the number of hairiness corresponding to different hairiness lengths in the yarn hairiness image according to the formula, respectively counting the number of the hairiness with the yarn hairiness lengths L of 1, 2, 3, 4, 5 and 6mm, evaluating the yarn hairiness by calculating the cv value, and finally obtaining a hairiness evaluation result.
Further, the preset connected region is a four-connected region.
Further, in the step 3, 7 layers of multi-layer wavelet decomposition is performed.
Further, the preset first condition includes the following three conditions, and the condition that the three conditions are met is determined as meeting the preset first condition: the condition 1, the pixel values and w around the central point can satisfy w is more than or equal to 2 and less than or equal to 6, and w is equal to the sum of the values of the rest pixels except the center of the template in the template;
in the condition 2, when pixel values around the central point are traversed anticlockwise, the times that two continuous pixel values are respectively 0 and 1 are 1;
and the condition 3 is that the product of pixel points at the upper, right, lower and lower positions of the central point and the lower, left and right positions is 0.
Further, the preset second condition includes the following three conditions, and the condition that the three conditions are met is determined as meeting the preset second condition:
the condition 1, the pixel values and w around the central point can satisfy that w is more than or equal to 2 and less than or equal to 6, and w is equal to the sum of the pixel values except the center of the template in the template;
in the condition 2, when pixel values around the central point are traversed anticlockwise, the times that two continuous pixel values are respectively 0 and 1 are 1;
the product of the condition 3, the upper left and lower three positions of the center point, and the upper left and right three position pixel point values is 0.
The invention has the beneficial effects that: the image processing method provided by the invention removes the influence of uneven illumination, extracts complete hair feather information, and counts the number of the pixels of each hair feather by dividing each hair feather into different areas, thereby calculating the length of the hair feather. Compared with the conventional method, the method can eliminate the influence of uneven illumination when the yarn image is collected, avoid the form influence of hairiness and improve the accuracy of hairiness detection.
Drawings
FIG. 1 is a raw yarn image;
FIG. 2 is a schematic view of a process for obtaining a final hair image;
FIG. 3 is a yarn gray scale image;
FIG. 4 is a yarn image wavelet decomposed image with different layer numbers;
FIG. 5 is a yarn image with the illumination effect removed;
FIG. 6 is a hairiness image without a yarn main;
FIG. 7 is a filtered hairiness image;
FIG. 8 is a binarized hairiness image;
FIG. 9 is a final hairiness image;
FIG. 10 is a matrix generated image;
fig. 11 is a partially enlarged view of fig. 10.
Detailed Description
The technical solution of the present invention will be described clearly and completely with reference to the accompanying drawings, and it should be understood that the described embodiments are only partial embodiments of the present invention, rather than full embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
1 yarn image acquisition
The method comprises the steps of collecting a yarn image by using an RH2000 video microscope, wherein the collected yarn image is clear, complete and reference. The proper size is also guaranteed under the condition of guaranteeing the definition, otherwise, the processing speed is influenced. Thus, with an accuracy of 1.0um, the resolution of the image is 1920 x 1200. Meanwhile, the yarn is ensured to be positioned in the middle of the camera visual field, and the axis is kept horizontal. And adjusting the light source and the focal length to make the image of the hairiness clearest. The original yarn image is shown in figure 1.
2 yarn image processing
2.1 yarn image processing flow chart
The video microscope is used for collecting the images of the yarns, and the brightness distribution of the collected yarn hairiness images is uneven due to the crossing of the yarn hairiness when the images are collected. In the collecting process, a small amount of noise can be generated due to the influence of the hairiness background, the subsequent yarn hairiness image processing can be influenced, and in order to eliminate the influence, the collected yarn hairiness image needs to be preprocessed. The flow chart is shown in figure 2.
2.2 extraction of hairiness texture
And performing low-frequency decomposition and reconstruction on the collected hairiness image by utilizing wavelet transformation to obtain illumination layer information, decomposing the image information into each layer by utilizing the wavelet decomposition, and selecting seven layers of low-frequency decomposition as the more layers of decomposition are, the less information is represented by the low-frequency image. As shown in fig. 4, the sixth layer can be approximately regarded as illumination information by the comparative analysis. And reconstructing a sixth layer of signal representation illumination information from the multi-layer two-dimensional wavelet decomposition detail coefficients, and eliminating the influence of illumination nonuniformity on subsequent image processing by using arithmetic operation, so that the hair feather features are more prominent, and the extraction of the hair feather is facilitated. The de-illuminated image is shown in fig. 5. And (4) performing morphological opening operation on the image five to obtain a trunk, removing the illumination yarn image by using the image 5 to subtract the trunk, and extracting a hairiness image without the trunk, as shown in fig. 6.
2.3 denoising the yarn hairiness
Because a small amount of noise exists in the image acquisition process, partial filoplume characteristics can be segmented and eliminated by directly carrying out binarization, and in order to carry out binarization segmentation on the image in FIG. 6 more accurately, the noise is filtered by using filtering, so that the filoplume characteristics are more obvious, and the result is shown in FIG. 7.
2.4 binarization processing
The method comprises the steps of carrying out improved Otsu method on seven hairiness images to obtain proper threshold values, carrying out segmentation, adding gray probability p (i) on the basis of an original Otsu algorithm, reducing the influence of the gray probability on average gray, obtaining proper threshold values, and changing the gray values lower than the threshold values into 0, so that the treated hairiness pixels are more prominent. And further obtaining a yarn hairiness binary image shown in the figure. The formula is as follows
Where p (i) represents the gray value probability, f (x, y) represents the pixel value of a certain point, and M × N represents the image size. Mu.s0Represents the average gray level of the foreground part, mu1Represents the background portion average gray scale, and μ represents the overall gray scale mean.
2.5 morphological treatment
Using a3 x 3 template to the obtained binary hair feather image to contain 9 pixel points, performing traversal processing on a hair feather pixel matrix, respectively performing two iterations by taking the pixel point with the value of 1 as the center of the template, and changing the pixel point value into 0 when the first iteration can meet the following conditions through judgment.
| A1 | A2 | A3 |
| A8 | P | A4 |
| A7 | A6 | A5 |
Condition 1: 2< ═ a1+ a2+ A3+ a4+ a5+ a6+ a7+ A8< ═ 6;
condition 2: when pixel values around the central point are traversed anticlockwise, the times that two continuous pixel values are respectively 0 and 1 are 1;
condition 3: A2A 4A 6 is 0
A4*A6*A8＝0；
The second iteration removes pixels that satisfy the following condition to change the value of 1 to 0.
Condition 1: 2< ═ A1+ A2+ A3+ A4+ A5+ A6+ A7+ A8< ═ 6,
Condition 2: when pixel values around the central point are traversed anticlockwise, the times that two continuous pixel values are respectively 0 and 1 are 1;
condition 3: A2A 4A 8 is 0
A2*A6*A8＝0；
The hairiness is thinned into a thin line through two iterations. Obtaining a complete hairiness image; as shown in fig. 9.
3 extracting and analyzing characteristic parameters of yarn hairiness
3.1 hairiness pixel statistics
Dividing the pixel number with the median value of 1 in the binarized hairiness image matrix of fig. 9 into different areas according to the communication condition, dividing the pixel number with the median value of 1 in the upper direction, the lower direction, the left direction and the right direction into one communication area according to the communication condition, marking different label numbers meeting the requirements of the communication areas, and finally returning to the matrix L and the total label number num, wherein the values of the labels are 1, 2 and num. And constructing a zero matrix S with a row and a maxArea column by calculating the maximum area pixel number maxArea, calculating the pixel number sum (num) of different label areas in the L matrix in a traversing way, calculating the pixel number sum (num) of each area as a column value for constructing the zero matrix S, and adding 1 at the position of the matrix as the number of hairiness. And (4) counting the binarization yarn hairiness matrix to finally obtain a matrix S taking a column as the number sum (num) of hairiness pixels, wherein the value at the corresponding position represents the number of the hairiness. The matrix S image is shown in fig. 10 and 11.
3.2 yarn hairiness statistics
The length of the yarn hairiness in the image is measured by a two-dimensional tool provided by a video microscope and is recorded as ls (mm), the diameter of the yarn hairiness is recorded as lv (mm), V represents the number of pixels of the yarn formed by single pixel points, namely the horizontal pixel value of the picture, and V is 1920 in the embodiment. The feather length L is (1mm,2mm,3mm,4mm,5mm,6mm) and the number sum of the feather pixels is calculated by the product of the number M of the formula pixels and the number of the feather diameter pixels. And dividing the hair feather length by using the pixel number sum corresponding to different hair feather lengths to realize hair feather rating. The statistical hairiness quantity calculation formula is as follows:
sum＝L*Lv*V^2/Ls^2
by dividing pixel number intervals corresponding to different hairiness lengths, as the column value of the S matrix corresponds to the number of the hairiness pixels, the matrix value of the S matrix in each interval is counted by utilizing a cyclic algorithm to obtain the hairiness length and the number of corresponding hairiness. Ranking hairiness using coefficient of variation cv
The experiment takes original yarn with the yarn density of 18.7tex, selects 6 yarn segments, collects 50 frames of images for each yarn segment, and after image processing, the pixel corresponding to each frame of image is 1920 x 1200, the actual length of each corresponding frame of yarn is about 10.25mm, and the actual length of each yarn segment is about 5 m. According to the yarn hairiness detection method, under the condition that no hairiness cross and special hairiness curl are considered, the image detection result is shown in table 1.
Table 1: results of image detection method
In order to verify the accuracy of the hairiness detection result of the method, the hairiness number of the yarn is counted by a visual method according to the length of 1m of the hairiness in the 6 yarn hairiness fragments in the table 1, and the result is shown in the table 2. The comparison shows that the error of the test result is within 6 percent compared with the visual method.
Table 2: image detection and visual method comparison
4 conclusion
The article provides video microscope imaging, and a yarn hairiness detection system with image processing is applied, so that the reliability of yarn hairiness detection can be effectively improved. Compared with the common hairiness detection method, the method can eliminate the influence of uneven illumination during image acquisition, is not influenced by the hairiness form, and improves the accuracy of hairiness detection. The test result shows that the method is practical and can provide reference for designing a yarn hairiness detection system in the future.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (5)
1. The yarn hairiness detection and rating method based on image processing is characterized by comprising the following steps of:
step 1, collecting an original yarn image;
step 2, carrying out gray level processing on the collected original yarn image to obtain a yarn gray level image;
step 3, performing multilayer wavelet decomposition reconstruction on the filtered yarn evenness image, approximately reconstructing an illumination information layer, eliminating the influence of uneven illumination through arithmetic operation, and obtaining a yarn image;
step 4, performing opening operation on the yarn image without the illumination influence in the step 3 to obtain a yarn main body, and extracting a hairiness image without the yarn main body by using mathematical operation; filtering and denoising the signal;
step 5, carrying out binarization on the yarn trunk image obtained in the step 4 by using an improved Dajin algorithm to obtain a clear hairiness image;
step 6, thinning the hairiness, using a3 x 3 template containing 9 pixel points, traversing a hairiness pixel matrix, taking the pixel point with the value of 1 as the center of the template, respectively carrying out two iterations, wherein the first iteration is to change the pixel point value meeting a preset first condition into 0, the second iteration is to change the pixel point value of the residual pixel point with the median value of 1 into 0 after removing the pixel point meeting a second preset condition, and thinning the hairiness into a thin line through the two iterations to obtain a complete hairiness image;
step 7, dividing each hairiness in the binarization hairiness image matrix into different areas for statistics, marking all hairiness pixels meeting the requirement of a preset connected area in the pixel matrix as the same label value, marking the hairiness pixels in the different connected areas as different label values, returning to the total label number num and the labeled hairiness image matrix L; calculating and storing the number of the hair feather pixels of each connected region and the number of the connected regions corresponding to the number of the pixels to obtain a matrix S by circularly traversing the number of the hair feather pixels of different regions in the pixel matrix;
step 8, measuring the length Ls of the yarn in the final hair feather image, wherein the diameter Lv and V of the hair feather represent the horizontal resolution of the final hair feather image, converting the length of the hair feather into a pixel number interval by finding the corresponding relation between the pixel number and the length, utilizing a formula sum to L x Lv x V ^2/Ls ^2, counting the matrix value of the pixel number in the corresponding interval of the matrix S by utilizing a circulation algorithm to obtain the hair feather quantity corresponding to different hair feather lengths, for example, when the number of the hair feather length in the interval of [ e mm, f mm ] is calculated, converting e mm into a pixel by utilizing the formula, converting f mm into b pixel, the value range of the pixel number of the hair feather length in the interval of [ e mm, f mm ] is [ a, b ], counting the hair feather quantity m1 of the pixel number of the hair feather in the interval of [ a, b ] in the matrix S, then m1 represents the length of the hair feather in [ e mm, f mm ] total number within the interval;
and 9, calculating the number of hairiness corresponding to different hairiness lengths in the yarn hairiness image according to the formula, evaluating the yarn hairiness by calculating the cv value, and finally obtaining a hairiness evaluation result.
2. The image-processing-based yarn hairiness detection rating method of claim 1, wherein the preset connected region is a four-connected region.
3. The method for detecting and grading yarn hairiness based on image processing as claimed in claim 1, wherein the step 3 is a 7-layer wavelet decomposition.
4. The method for detecting and rating the yarn hairiness based on the image processing as claimed in claim 1, wherein the preset first condition comprises the following three conditions, and the satisfaction of the three conditions is determined as the satisfaction of the preset first condition: the condition 1, the pixel values and w around the central point can satisfy w is more than or equal to 2 and less than or equal to 6, and w is equal to the sum of the values of the rest pixels except the center of the template in the template;
in the condition 2, when pixel values around the central point are traversed anticlockwise, the times that two continuous pixel values are respectively 0 and 1 are 1;
and the condition 3 is that the product of pixel points at the upper, right, lower and lower positions of the central point and the lower, left and right positions is 0.
5. The image-processing-based yarn hairiness detection and rating method according to claim 1, wherein the preset second condition comprises the following three conditions, and the satisfaction of the three conditions is determined as the satisfaction of the preset second condition:
the condition 1, the sum of the pixel values w around the central point can satisfy 2-6, w is equal to the sum of the pixel values of the rest of the template except the center of the template;
in the condition 2, when pixel values around the central point are traversed anticlockwise, the times that two continuous pixel values are respectively 0 and 1 are 1; the product of the condition 3, the upper left and lower three positions of the center point, and the upper left and right three position pixel point values is 0.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114998321A (en) * | 2022-07-19 | 2022-09-02 | 南通博纳纺织品有限公司 | Textile material surface hairiness degree identification method based on optical means |
| CN115082656A (en) * | 2022-08-23 | 2022-09-20 | 江苏万喜登家居科技有限公司 | Yarn hairiness damage identification method in spinning process and data processing system |
| CN115082489A (en) * | 2022-08-23 | 2022-09-20 | 江苏永银化纤有限公司 | Colored silk evaluation method |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2010082245A (en) * | 2008-09-30 | 2010-04-15 | Panasonic Electric Works Co Ltd | Hair information measuring method |
| CN107515220A (en) * | 2017-08-02 | 2017-12-26 | 江南大学 | A kind of detection of yarn black filoplume amount and evaluation method based on image procossing |
| CN112330673A (en) * | 2020-12-11 | 2021-02-05 | 武汉纺织大学 | Woven fabric density detection method based on image processing |
-
2021
- 2021-02-08 CN CN202110183704.7A patent/CN113012105B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2010082245A (en) * | 2008-09-30 | 2010-04-15 | Panasonic Electric Works Co Ltd | Hair information measuring method |
| CN107515220A (en) * | 2017-08-02 | 2017-12-26 | 江南大学 | A kind of detection of yarn black filoplume amount and evaluation method based on image procossing |
| CN112330673A (en) * | 2020-12-11 | 2021-02-05 | 武汉纺织大学 | Woven fabric density detection method based on image processing |
Non-Patent Citations (1)
| Title |
|---|
| 方珩;辛斌杰;刘晓霞;张杰;: "一种新型纱线毛羽图像特征识别算法的研究", 河北科技大学学报, no. 01, 31 December 2015 (2015-12-31) * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114998321A (en) * | 2022-07-19 | 2022-09-02 | 南通博纳纺织品有限公司 | Textile material surface hairiness degree identification method based on optical means |
| CN115082656A (en) * | 2022-08-23 | 2022-09-20 | 江苏万喜登家居科技有限公司 | Yarn hairiness damage identification method in spinning process and data processing system |
| CN115082489A (en) * | 2022-08-23 | 2022-09-20 | 江苏永银化纤有限公司 | Colored silk evaluation method |
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